English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Efficient discovery of a speaker's emotional states in a multi-partyconversation is significant to design human-like conversational agents. Duringa conversation, the cognitive state of a speaker often alters due to certainpast utterances, which may lead to a flip in their emotional state. Therefore,discovering the reasons (triggers) behind the speaker's emotion-flip during aconversation is essential to explain the emotion labels of individualutterances. In this paper, along with addressing the task of emotionrecognition in conversations (ERC), we introduce a novel task - Emotion-FlipReasoning (EFR), that aims to identify past utterances which have triggeredone's emotional state to flip at a certain time. We propose a masked memorynetwork to address the former and a Transformer-based network for the lattertask. To this end, we consider MELD, a benchmark emotion recognition dataset inmulti-party conversations for the task of ERC, and augment it with newground-truth labels for EFR. An extensive comparison with five state-of-the-artmodels suggests improved performances of our models for both tasks. We furtherpresent anecdotal evidence and both qualitative and quantitative error analysesto support the superiority of our models compared to the baselines.

Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer